Reducing the Bias in DRSS-Based Localization: An Instrumental Variable Approach

Abstract

This paper proposes a closed-form solution with reduced bias for differential received signal strength (DRSS) localization. During the linearization of DRSS measurement equations, the measurement noise is injected into the measurement data matrix, resulting in a correlation between the measurement noise and measurement data matrix. Existing closed-form solutions do not consider this correlation, which causes biased estimation results. The solution proposed here aims to eliminate the bias by introducing instrument variables (IV), whose role is to mitigate the correlation arising from linearization. Simulation results demonstrate the improved performance of the IV-based estimator over some existing closed-form solutions, in the form of root-mean-squared errors that are close to the Cramér-Rao lower bound, and significantly reduced bias, over a wide range of noise levels.